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Marine Biology

, Volume 154, Issue 2, pp 373–381 | Cite as

Pyruvate-utilizing bacteria as potential contributors to the food web in the Arabian Gulf

  • H. A. Al-Sarawi
  • H. M. Mahmoud
  • S. S. Radwan
Research Article
  • 94 Downloads

Abstract

Water samples from the Kuwaiti coasts of the Arabian Gulf were used for counting and isolating bacteria capable of growth on low molecular weight organic compounds known to be released by picocyanobacteria. The compounds tested were potassium acetate, sodium pyruvate, fumaric acid, succinic acid, sodium citrate and glycerol. For comparison, the bacterial numbers on glucose (a conventional carbon source) and Tween 80 and crude oil (unconventional carbon sources), as sole sources of carbon and energy were also determined. Sodium pyruvate was, in most cases, the carbon and energy source most commonly utilized by the cultivable surface water bacteria. The most common cultivable bacterial genera on the test carbon sources were Pseudoalteromonas, Vibrio, Cobetia and Roseobacter. Less common genera were Rhodococcus, Pseudomonas and Bacillus. Quantification of heterotrophic bacteria associated with cultures of local picocyanobacterial strains, originally isolated from the Gulf surface water, also revealed that the carbon source most commonly utilized by cultivable bacteria was sodium pyruvate. However, a different bacterial composition was identified, with Alcanivorax, Stappia and Marinobacter as the major heterotrophic genera. All heterotrophic bacteria that grew on sodium pyruvate could also grow on β-alanine, as sole sources of carbon and energy. From this study we suggest that the Arabian Gulf bacteria utilizing picocyanobacterial low molecular weight compounds, particularly pyruvate may potentially contribute to the food web in this aquatic system.

Keywords

Biochemical Oxygen Demand Synechococcus Sodium Pyruvate Dissolve Organic Matter Pyruvic Acid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Thanks are due to Mrs. S. Salamah, Mrs. M. Khanafer and Ms. R. Suliman for the excellent technical assistance. This work has been supported by Kuwait University, Research project number YS04/04.

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • H. A. Al-Sarawi
    • 1
  • H. M. Mahmoud
    • 1
  • S. S. Radwan
    • 1
  1. 1.Department of Biological Sciences, Faculty of ScienceKuwait UniversitySafatKuwait

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